Bibliographic record
Abstract
Background: Following a patient safety incident, patients and families need disclosure and reconciliation, practitioners need debriefing, and the organization needs to implement changes to prevent further errors. In order to substantiate the Canadian Incident Analysis Framework, the Canadian Patient Safety Institute (CPSI) invited Queen's Joanna Briggs Collaboration (QJBC) to work collaboratively with them to provide a synthesis of the current literature on the frameworks, models or tools that address patient safety incident management. Objective: To perform a mapping review of the international literature focused on frameworks, models or tools for patient safety incident management across the continuum of care. Methods: We followed Joanna Briggs Institute method of synthesis. A 3-step approach was used to search the peer-review literature: 1) initial search of MEDLINE and CINAHL; 2) detailed search across all databases using all keywords and index terms; 3) hand search the reference lists of includedpapersto locate additional studies. Targeted databases included: CINAHL; Medline; EMBASE; PsycINFO; AMED; Cochrane; Web of Science, Ageline; GlobalHealth; and Social Sciences Abstracts. Search strategies were tested for sensitivity and specificity by QJBC Library Scientist using the Peer Review of Electronic Search Strategies (PRESS) methodology developed by Canadian Agency for Drugs and Technologies in Health. Results: The search located 4,413 citations that provided 21 papers. Publication dates spanned 1999–2012 and included 10 theoretical papers, seven descriptive studies, three reviews, and one qualitative study. Thirteen models, four frameworks and four tools were identified. Discussion: A range of approaches were described including psychological and cognitive frameworks, statistical models, and human factors approaches. Only one model had been formally evaluated and implemented in several settings. Conclusion: To advance our knowledge in this area existing models, frameworks and tools need to be evaluated and implemented. Further research would be beneficial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".